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network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
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loop/TAD structures. - Perform comparative analyses versus Populus tremula; apply network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce
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of the protonation states is a prerequisite for mechanistic understanding, computational chemistry methods are crucial for modelling the reaction trajectory. We have previously used quantum mechanics-molecular
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imaging to resolve molecular structure and guide engineering Protein design and molecular modeling, supported by AI tools such as AlphaFold2 Development of functional assays in insect and bacterial systems
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variants High-resolution cryo-EM and cryo-ET imaging to resolve molecular structure and guide engineering Protein design and molecular modeling, supported by AI tools such as AlphaFold2 Development
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of interest (but not limited to): Intersection of cybersecurity and AI, i.e., attacks and defenses leveraging AI solutions, or attacks and defenses within AI solutions (e.g., backdooring, model poisoning
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cluster / micellar phases and arrested phase separation. We plan for theory-oriented work, combining analytical modeling based on liquid theory and coarse-grained simulations to explain experimental
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additional specific requirements that candidates have to fulfill, be sure to check what these are before you apply. Additional requirements: Very good oral and written proficiency in English. Practical
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separation. We plan for theory-oriented work, combining analytical modeling based on liquid theory and coarse-grained simulations to explain experimental phenomenology. Connection to experimental work is
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check what these are before you apply. Additional requirements: Very good oral and written proficiency in English. Practical experience of studies of complex systems, including cells Practical experience